ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Unsupervised Learning of Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Object, scene and actions: combining multiple features for human action recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Activities as time series of human postures
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Pairwise Features for Human Action Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Action Recognition Using Mined Hierarchical Compound Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Aggregated search in graph databases: preliminary results
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Graph aggregation based image modeling and indexing for video annotation
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Frequent approximate subgraphs as features for graph-based image classification
Knowledge-Based Systems
Selective spatio-temporal interest points
Computer Vision and Image Understanding
Action recognition by dense trajectories
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A "string of feature graphs" model for recognition of complex activities in natural videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Real-Time exact graph matching with application in human action recognition
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
A SURF-Based spatio-temporal feature for feature-fusion-based action recognition
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Classification improvement of local feature vectors over the KNN algorithm
Multimedia Tools and Applications
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Due to the exponential growth of the video data stored and uploaded in the Internet websites especially YouTube, an effective analysis of video actions has become very necessary. In this paper, we tackle the challenging problem of human action recognition in realistic video sequences. The proposed system combines the efficiency of the Bag-of-visual-Words strategy and the power of graphs for structural representation of features. It is built upon the commonly used Space-Time Interest Points (STIP) local features followed by a graph-based video representation which models the spatio-temporal relations among these features. The experiments are realized on two challenging datasets: Hollywood2 and UCF YouTube Action. The experimental results show the effectiveness of the proposed method.